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	%THE TITLE PAGE & STUFF_________________________________________________________________
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	\rule{\linewidth}{1px}\\[10pt]
	\Huge{Texture Synthesis for Material Classification}
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	\vspace*{1cm}
	\large{Master\rq s Thesis in Artificial Intelligence -- Intelligent Systems}
	\large
	\vspace*{4cm}
	\begin{multicols}{2}
		\begin{flushleft}
			{\emph{Author:}\\
			Jasper van Turnhout\\
			\textit{jturnhou@science.uva.nl}\\ 
			Student number: 0312649}  
		\end{flushleft}
		\begin{flushleft}
			{\emph{Supervisors:}\\
			\href{mailto:Th.Gevers@uva.nl}{Prof. dr. Theo Gevers}\\
			\href{mailto:geusebroekuva.nl}{Dr. Jan-Mark Geusebroek}\\
			Informatics Institute, Faculty of Science, University of Amsterdam\\
			}
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			\section*{Abstract}
			In this thesis, the problem of material classification from single images is addressed and how image synthesis can support this research field. Previous research shows how one can derive features based on Gaussian filter responses from material images and obtain high classification accuracies using classifiers such as Nearest Neighbor, Naive Bayes or Support Vector Machines. To model the appearance of material under different illumination conditions a lot of data is needed to obtain satisfactory models. \\[10px]

To obtain robust features from material data that covers the intra-class variability, we need huge amounts of data recorded under controlled conditions. Since this recording is done manually, this becomes an intensive task. Rather than recording this novel data, one could synthesize this data using surface albedo and surface normal information, recovered from image data. \\[10px]

In previous research it has been shown that synthetic image data can be used to derive material models for various diffuse material classes. The image data is synthesized using a simple Lambertian reflection model. In this research, the quality of synthetic image data is investigated for more complex reflectance models with respect to Lambertian reflectance. Significant improvements have been reported for the more complex reflectance models. However, we suspect that the improvements are limited by the simple photometric stereo method which recovers the surface albedo and surface normals --- this suggests that future work needs better methods for the recovery of the albedo and surface normals.
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	\chapter{Introduction}
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	\chapter{Related work}
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	\chapter{Fundamentals}
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	\chapter{Approach}
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	\chapter{Empirical Models}
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	\chapter{Microfacet Models}
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	\chapter{Experiments}
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	\chapter{Conclusion \& Future Work}
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